Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory
The gigantically unexplored chemical space for the combinations of donor and acceptor poses a mammoth challenge in enhancing power conversion efficiency. Herein, a strategy integrating machine learning and density functional theory was proposed to assist the rational screening of prominent donor/acc...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127522001824 |
| _version_ | 1818126380822429696 |
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| author | Xiujuan Liu Yueyue Shao Tian Lu Dongping Chang Minjie Li Wencong Lu |
| author_facet | Xiujuan Liu Yueyue Shao Tian Lu Dongping Chang Minjie Li Wencong Lu |
| author_sort | Xiujuan Liu |
| collection | DOAJ |
| description | The gigantically unexplored chemical space for the combinations of donor and acceptor poses a mammoth challenge in enhancing power conversion efficiency. Herein, a strategy integrating machine learning and density functional theory was proposed to assist the rational screening of prominent donor/acceptor pairs. Notably, 10 promising donor/acceptor pairs were ultimately sieved out with power conversion efficiency lager than 18.22%. Furthermore, the density functional theory computation expressed that the new donor/acceptor pair of D18/BTP-eC9 possessed stronger absorption intensity, while the KCS/KCR (ratio of charge separation rate and charge recombination rate) and μe (electron mobility) were larger than those of the best known D18/Y6 with the increments of 201.31% and 125.98% respectively. The SHapley Additive exPlanations analysis revealed that the two most important features of A_nR08 and D_D/Dtr12 were positively related to the improvement of power conversion efficiency. It was found that A_nR08 was positively associated with π-conjugation strength, and high D_D/Dtr12 suggested the highly fused chemical structure, indicating the practical clues in discovering top-performing donor/acceptor pairs. |
| first_indexed | 2024-12-11T07:00:33Z |
| format | Article |
| id | doaj.art-da3c5252a1ed4a4e8edddeee1a9b1521 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-12-11T07:00:33Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-da3c5252a1ed4a4e8edddeee1a9b15212022-12-22T01:16:37ZengElsevierMaterials & Design0264-12752022-04-01216110561Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theoryXiujuan Liu0Yueyue Shao1Tian Lu2Dongping Chang3Minjie Li4Wencong Lu5Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, ChinaDepartment of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, ChinaMaterials Genome Institute, Shanghai University, Shanghai 200444, ChinaMaterials Genome Institute, Shanghai University, Shanghai 200444, ChinaDepartment of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China; Zhejiang Laboratory, Hangzhou 311100, China; Corresponding authors at: Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China; Materials Genome Institute, Shanghai University, Shanghai 200444, China; Zhejiang Laboratory, Hangzhou 311100, China; Corresponding authors at: Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.The gigantically unexplored chemical space for the combinations of donor and acceptor poses a mammoth challenge in enhancing power conversion efficiency. Herein, a strategy integrating machine learning and density functional theory was proposed to assist the rational screening of prominent donor/acceptor pairs. Notably, 10 promising donor/acceptor pairs were ultimately sieved out with power conversion efficiency lager than 18.22%. Furthermore, the density functional theory computation expressed that the new donor/acceptor pair of D18/BTP-eC9 possessed stronger absorption intensity, while the KCS/KCR (ratio of charge separation rate and charge recombination rate) and μe (electron mobility) were larger than those of the best known D18/Y6 with the increments of 201.31% and 125.98% respectively. The SHapley Additive exPlanations analysis revealed that the two most important features of A_nR08 and D_D/Dtr12 were positively related to the improvement of power conversion efficiency. It was found that A_nR08 was positively associated with π-conjugation strength, and high D_D/Dtr12 suggested the highly fused chemical structure, indicating the practical clues in discovering top-performing donor/acceptor pairs.http://www.sciencedirect.com/science/article/pii/S0264127522001824Machine learningOrganic Solar CellsShapley Additive exPlanationsDensity Functional TheoryDonor/Acceptor interface |
| spellingShingle | Xiujuan Liu Yueyue Shao Tian Lu Dongping Chang Minjie Li Wencong Lu Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory Materials & Design Machine learning Organic Solar Cells Shapley Additive exPlanations Density Functional Theory Donor/Acceptor interface |
| title | Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory |
| title_full | Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory |
| title_fullStr | Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory |
| title_full_unstemmed | Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory |
| title_short | Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory |
| title_sort | accelerating the discovery of high performance donor acceptor pairs in photovoltaic materials via machine learning and density functional theory |
| topic | Machine learning Organic Solar Cells Shapley Additive exPlanations Density Functional Theory Donor/Acceptor interface |
| url | http://www.sciencedirect.com/science/article/pii/S0264127522001824 |
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